Browsing by Author "Avci, Isa"

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Intelligent Transportation System Technologies, Challenges and Security
(Mdpi, 2024) Avci, Isa; Koca, Murat
Intelligent Transportation Systems (ITS) first appeared in 1868 with traffic lights. With developing technology, the need to bring a smart approach to transportation applications within the scope of speed and environmental protection has emerged. Protecting ITS infrastructure against cyber attacks has become a matter of reputation for states. It is essential to provide the necessary technological infrastructure for the integrated operation of the systems used in ITS, especially geographical location, communication, and mapping. These technological developments bring cyber attacks, risks, and many dangers that should be avoided, especially on the systems used. This study examines ITS architecture, applications, communication technologies, and new trend technologies in detail. This study includes contributing to studies in the field of ITS and preventing attacks and incidents that may occur in terms of cyber security. The most important cyber attacks that may occur in ITS applications are included. In addition, the minimum security requirements that can be taken in ITS applications and infrastructures against these attacks are included.
A Novel Hybrid Model Detection of Security Vulnerabilities in Industrial Control Systems and Iot Using Gcn Plus Lstm
(Ieee-inst Electrical Electronics Engineers inc, 2024) Koca, Murat; Avci, Isa
In this study, we address critical security vulnerabilities in Industrial Control Systems (ICS) and the Internet of Things (IoT) by focusing on enhancing collaboration and communication among interconnected devices. Recognizing the inherent risks and the sophisticated nature of cyber threats in such environments, we introduce a novel and complex implementation that leverages the synergistic potential of Graph Convolutional Networks (GCN) and Long Short-Term Memory (LSTM) models. This approach is designed to intelligently predict and detect intrusion attempts by analyzing the dynamic interactions and data flow within networked systems. Our methodology not only differentiates between the operational nuances of various IoT routing mechanisms but also tackles the core design challenges faced by ICS. Through rigorous experimentation, including the deployment of our model in simulated high-risk scenarios, we have demonstrated its efficacy in identifying and mitigating deceptive connectivity disruptions with a remarkable accuracy rate of 99.99%. This performance underscores the models capability to serve as a robust security layer, ensuring the integrity and resilience of ICS networks against sophisticated cyber threats. Our findings contribute a significant advancement in the field of cybersecurity for ICS and IoT, proposing a comprehensive framework that can be centrally integrated with existing security information and incident management systems for enhanced protective measures.
A Novel Security Risk Analysis Using the Ahp Method in Smart Railway Systems
(Mdpi, 2024) Avci, Isa; Koca, Murat
Transportation has an essential place in societies and importance to people in terms of its social and economic aspects. Innovative rail systems need to be integrated with developing technologies for transportation. Systemic failures, personnel errors, sabotage, and cyber-attacks in the techniques used will cause a damaged corporate reputation and revenue losses. In this study, cybersecurity attack methods in smart rail systems were determined, and cyber events occurring worldwide through these technologies were analyzed. Risk analysis in terms of transportation safety in smart rail systems was determined by considering the opinions of 10 different experts along with the Analytic Hierarchical Process (AHP) performance criteria. Informatics experts were selected from a group of people with at least 5-15 years of experience. According to these risk analysis calculations, cybersecurity stood out as the most critical security risk at 27.74%. Other risky areas included physical security, calculated at 14.59%, operator errors at 16.20%, and environmental security at 10.93%.
Predicting Ddos Attacks Using Machine Learning Algorithms in Building Management Systems
(Mdpi, 2023) Avci, Isa; Koca, Murat
The rapid growth of the Internet of Things (IoT) in smart buildings necessitates the continuous evaluation of potential threats and their implications. Conventional methods are increasingly inadequate in measuring risk and mitigating associated hazards, necessitating the development of innovative approaches. Cybersecurity systems for IoT are critical not only in Building Management System (BMS) applications but also in various aspects of daily life. Distributed Denial of Service (DDoS) attacks targeting core BMS software, particularly those launched by botnets, pose significant risks to assets and safety. In this paper, we propose a novel algorithm that combines the power of the Slime Mould Optimization Algorithm (SMOA) for feature selection with an Artificial Neural Network (ANN) predictor and the Support Vector Machine (SVM) algorithm. Our enhanced algorithm achieves an outstanding accuracy of 97.44% in estimating DDoS attack risk factors in the context of BMS. Additionally, it showcases a remarkable 99.19% accuracy in predicting DDoS attacks, effectively preventing system disruptions, and managing cyber threats. To further validate our work, we perform a comparative analysis using the K-Nearest Neighbor Classifier (KNN), which yields an accuracy rate of 96.46%. Our model is trained on the Canadian Institute for Cybersecurity (CIC) IoT Dataset 2022, enabling behavioral analysis and vulnerability testing on diverse IoT devices utilizing various protocols, such as IEEE 802.11, Zigbee-based, and Z-Wave.